Information about single file
A dagbag is a collection of dags, parsed out of a folder tree and has high
- class airflow.models.dagbag.FileLoadStat¶
Information about single file
- file :str¶
- duration :datetime.timedelta¶
- dag_num :int¶
- task_num :int¶
- dags :str¶
- class airflow.models.dagbag.DagBag(dag_folder=None, include_examples=conf.getboolean('core', 'LOAD_EXAMPLES'), include_smart_sensor=conf.getboolean('smart_sensor', 'USE_SMART_SENSOR'), safe_mode=conf.getboolean('core', 'DAG_DISCOVERY_SAFE_MODE'), read_dags_from_db=False, store_serialized_dags=None, load_op_links=True)¶
A dagbag is a collection of dags, parsed out of a folder tree and has high level configuration settings, like what database to use as a backend and what executor to use to fire off tasks. This makes it easier to run distinct environments for say production and development, tests, or for different teams or security profiles. What would have been system level settings are now dagbag level so that one system can run multiple, independent settings sets.
include_examples (bool) – whether to include the examples that ship with airflow or not
include_smart_sensor (bool) – whether to include the smart sensor native DAGs that create the smart sensor operators for whole cluster
read_dags_from_db (bool) – Read DAGs from DB if
Trueis passed. If
FalseDAGs are read from python files.
load_op_links (bool) – Should the extra operator link be loaded via plugins when de-serializing the DAG? This flag is set to False in Scheduler so that Extra Operator links are not loaded to not run User code in Scheduler.
- property store_serialized_dags(self)¶
Whether or not to read dags from DB
- property dag_ids(self)¶
a list of DAG IDs in this bag
- Return type
- get_dag(self, dag_id, session=None)¶
Gets the DAG out of the dictionary, and refreshes it if expired
dag_id – DAG Id
- process_file(self, filepath, only_if_updated=True, safe_mode=True)¶
Given a path to a python module or zip file, this method imports the module and look for dag objects within it.
- bag_dag(self, dag, root_dag)¶
Adds the DAG into the bag, recurses into sub dags.
AirflowDagCycleException if a cycle is detected in this dag or its subdags.
AirflowDagDuplicatedIdException if this dag or its subdags already exists in the bag.
- collect_dags(self, dag_folder=None, only_if_updated=True, include_examples=conf.getboolean('core', 'LOAD_EXAMPLES'), include_smart_sensor=conf.getboolean('smart_sensor', 'USE_SMART_SENSOR'), safe_mode=conf.getboolean('core', 'DAG_DISCOVERY_SAFE_MODE'))¶
Given a file path or a folder, this method looks for python modules, imports them and adds them to the dagbag collection.
Note that if a
.airflowignorefile is found while processing the directory, it will behave much like a
.gitignore, ignoring files that match any of the patterns specified in the file.
Note: The patterns in
.airflowignoreare interpreted as either un-anchored regexes or gitignore-like glob expressions, depending on the
Collects DAGs from database.
Prints a report around DagBag loading stats
- sync_to_db(self, session=None)¶
Save attributes about list of DAG to the DB.